






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import DetrImageProcessor


class 							__UpperCamelCase     (  unittest.TestCase ):







	def __init__(	self   :				Union[str, Any]							,					_lowerCAmelCase   :				Tuple							,					_lowerCAmelCase   :				int=7							,					_lowerCAmelCase   :				int=3							,					_lowerCAmelCase   :				Union[str, Any]=30							,					_lowerCAmelCase   :				List[Any]=400							,					_lowerCAmelCase   :				int=True							,					_lowerCAmelCase   :				Dict=None							,					_lowerCAmelCase   :				str=True							,					_lowerCAmelCase   :				Dict=1 / 255							,					_lowerCAmelCase   :				Union[str, Any]=True							,					_lowerCAmelCase   :				Dict=[0.5, 0.5, 0.5]							,					_lowerCAmelCase   :				List[Any]=[0.5, 0.5, 0.5]							,					_lowerCAmelCase   :				List[Any]=True							,					)		->			str:
		"""simple docstring"""







		__lowercase   							=	size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
		__lowercase   							=	parent
		__lowercase   							=	batch_size
		__lowercase   							=	num_channels
		__lowercase   							=	min_resolution
		__lowercase   							=	max_resolution
		__lowercase   							=	do_resize
		__lowercase   							=	size
		__lowercase   							=	do_rescale
		__lowercase   							=	rescale_factor
		__lowercase   							=	do_normalize
		__lowercase   							=	image_mean
		__lowercase   							=	image_std
		__lowercase   							=	do_pad







	def 			_a (	self   :				Any      )		->			Dict:
		"""simple docstring"""







		return {
		    "do_resize": self.do_resize,
		    "size": self.size,
		    "do_rescale": self.do_rescale,
		    "rescale_factor": self.rescale_factor,
		    "do_normalize": self.do_normalize,
		    "image_mean": self.image_mean,
		    "image_std": self.image_std,
		    "do_pad": self.do_pad,
		}







	def 			_a (	self   :				Any							,					_lowerCAmelCase   :				Tuple							,					_lowerCAmelCase   :				Dict=False      )		->			List[str]:
		"""simple docstring"""







		if not batched:
			__lowercase   							=	image_inputs[0]
			if isinstance(_lowerCAmelCase							,					Image.Image      ):
				__lowercase							,    __lowercase   							=	image.size
			else:
				__lowercase							,    __lowercase   							=	image.shape[1], image.shape[2]
			if w < h:
				__lowercase   							=	int(self.size["""shortest_edge"""] * h / w      )
				__lowercase   							=	self.size["""shortest_edge"""]
			elif w > h:
				__lowercase   							=	self.size["""shortest_edge"""]
				__lowercase   							=	int(self.size["""shortest_edge"""] * w / h      )
			else:
				__lowercase   							=	self.size["""shortest_edge"""]
				__lowercase   							=	self.size["""shortest_edge"""]

		else:
			__lowercase   							=	[]
			for image in image_inputs:
				__lowercase							,    __lowercase   							=	self.get_expected_values([image]      )
				expected_values.append((expected_height, expected_width)      )
			__lowercase   							=	max(_lowerCAmelCase							,					key=lambda _lowerCAmelCase      : item[0]      )[0]
			__lowercase   							=	max(_lowerCAmelCase							,					key=lambda _lowerCAmelCase      : item[1]      )[1]

		return expected_height, expected_width


@require_torch
@require_vision
class 							__UpperCamelCase     (  _lowerCAmelCase ,      unittest.TestCase ):
	__snake_case						:Optional[Any] =							DetrImageProcessor if is_vision_available() else None







	def 			_a (	self   :				Any      )		->			Union[str, Any]:
		"""simple docstring"""







		__lowercase   							=	DetrImageProcessingTester(self      )







	@property
	def 			_a (	self   :				Optional[int]      )		->			Optional[Any]:
		"""simple docstring"""







		return self.image_processor_tester.prepare_image_processor_dict()







	def 			_a (	self   :				Union[str, Any]      )		->			Optional[int]:
		"""simple docstring"""







		__lowercase   							=	self.image_processing_class(**self.image_processor_dict      )
		self.assertTrue(hasattr(_lowerCAmelCase							,					"""image_mean"""      )      )
		self.assertTrue(hasattr(_lowerCAmelCase							,					"""image_std"""      )      )
		self.assertTrue(hasattr(_lowerCAmelCase							,					"""do_normalize"""      )      )
		self.assertTrue(hasattr(_lowerCAmelCase							,					"""do_rescale"""      )      )
		self.assertTrue(hasattr(_lowerCAmelCase							,					"""rescale_factor"""      )      )
		self.assertTrue(hasattr(_lowerCAmelCase							,					"""do_resize"""      )      )
		self.assertTrue(hasattr(_lowerCAmelCase							,					"""size"""      )      )
		self.assertTrue(hasattr(_lowerCAmelCase							,					"""do_pad"""      )      )







	def 			_a (	self   :				Dict      )		->			Union[str, Any]:
		"""simple docstring"""







		__lowercase   							=	self.image_processing_class.from_dict(self.image_processor_dict      )
		self.assertEqual(image_processor.size							,					{"""shortest_edge""": 18, """longest_edge""": 1333}      )
		self.assertEqual(image_processor.do_pad							,					_lowerCAmelCase      )

		__lowercase   							=	self.image_processing_class.from_dict(
		    self.image_processor_dict							,					size=42							,					max_size=84							,					pad_and_return_pixel_mask=_lowerCAmelCase      )
		self.assertEqual(image_processor.size							,					{"""shortest_edge""": 42, """longest_edge""": 84}      )
		self.assertEqual(image_processor.do_pad							,					_lowerCAmelCase      )







	def 			_a (	self   :				Any      )		->			Tuple:
		"""simple docstring"""







		pass







	def 			_a (	self   :				str      )		->			Dict:
		"""simple docstring"""







		__lowercase   							=	self.image_processing_class(**self.image_processor_dict      )
		# create random PIL images
		__lowercase   							=	prepare_image_inputs(self.image_processor_tester							,					equal_resolution=_lowerCAmelCase      )
		for image in image_inputs:
			self.assertIsInstance(_lowerCAmelCase							,					Image.Image      )

		# Test not batched input
		__lowercase   							=	image_processing(image_inputs[0]							,					return_tensors="""pt"""      ).pixel_values

		__lowercase							,    __lowercase   							=	self.image_processor_tester.get_expected_values(_lowerCAmelCase      )

		self.assertEqual(
		    encoded_images.shape							,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,					)

		# Test batched
		__lowercase							,    __lowercase   							=	self.image_processor_tester.get_expected_values(_lowerCAmelCase							,					batched=_lowerCAmelCase      )

		__lowercase   							=	image_processing(_lowerCAmelCase							,					return_tensors="""pt"""      ).pixel_values
		self.assertEqual(
		    encoded_images.shape							,					(
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        expected_height,
		        expected_width,
		    )							,					)







	def 			_a (	self   :				str      )		->			Tuple:
		"""simple docstring"""







		__lowercase   							=	self.image_processing_class(**self.image_processor_dict      )
		# create random numpy tensors
		__lowercase   							=	prepare_image_inputs(self.image_processor_tester							,					equal_resolution=_lowerCAmelCase							,					numpify=_lowerCAmelCase      )
		for image in image_inputs:
			self.assertIsInstance(_lowerCAmelCase							,					np.ndarray      )

		# Test not batched input
		__lowercase   							=	image_processing(image_inputs[0]							,					return_tensors="""pt"""      ).pixel_values

		__lowercase							,    __lowercase   							=	self.image_processor_tester.get_expected_values(_lowerCAmelCase      )

		self.assertEqual(
		    encoded_images.shape							,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,					)

		# Test batched
		__lowercase   							=	image_processing(_lowerCAmelCase							,					return_tensors="""pt"""      ).pixel_values

		__lowercase							,    __lowercase   							=	self.image_processor_tester.get_expected_values(_lowerCAmelCase							,					batched=_lowerCAmelCase      )

		self.assertEqual(
		    encoded_images.shape							,					(
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        expected_height,
		        expected_width,
		    )							,					)







	def 			_a (	self   :				Optional[Any]      )		->			Optional[int]:
		"""simple docstring"""







		__lowercase   							=	self.image_processing_class(**self.image_processor_dict      )
		# create random PyTorch tensors
		__lowercase   							=	prepare_image_inputs(self.image_processor_tester							,					equal_resolution=_lowerCAmelCase							,					torchify=_lowerCAmelCase      )
		for image in image_inputs:
			self.assertIsInstance(_lowerCAmelCase							,					torch.Tensor      )

		# Test not batched input
		__lowercase   							=	image_processing(image_inputs[0]							,					return_tensors="""pt"""      ).pixel_values

		__lowercase							,    __lowercase   							=	self.image_processor_tester.get_expected_values(_lowerCAmelCase      )

		self.assertEqual(
		    encoded_images.shape							,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,					)

		# Test batched
		__lowercase   							=	image_processing(_lowerCAmelCase							,					return_tensors="""pt"""      ).pixel_values

		__lowercase							,    __lowercase   							=	self.image_processor_tester.get_expected_values(_lowerCAmelCase							,					batched=_lowerCAmelCase      )

		self.assertEqual(
		    encoded_images.shape							,					(
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        expected_height,
		        expected_width,
		    )							,					)







	@slow
	def 			_a (	self   :				Optional[Any]      )		->			Dict:
		"""simple docstring"""







		__lowercase   							=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
		with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""							,					"""r"""      ) as f:
			__lowercase   							=	json.loads(f.read()      )

		__lowercase   							=	{"""image_id""": 3_9769, """annotations""": target}

		# encode them
		__lowercase   							=	DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50"""      )
		__lowercase   							=	image_processing(images=_lowerCAmelCase							,					annotations=_lowerCAmelCase							,					return_tensors="""pt"""      )

		# verify pixel values
		__lowercase   							=	torch.Size([1, 3, 800, 1066]      )
		self.assertEqual(encoding["""pixel_values"""].shape							,					_lowerCAmelCase      )

		__lowercase   							=	torch.tensor([0.2_796, 0.3_138, 0.3_481]      )
		self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]							,					_lowerCAmelCase							,					atol=1e-4      )      )

		# verify area
		__lowercase   							=	torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]      )
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]							,					_lowerCAmelCase      )      )
		# verify boxes
		__lowercase   							=	torch.Size([6, 4]      )
		self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape							,					_lowerCAmelCase      )
		__lowercase   							=	torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]      )
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]							,					_lowerCAmelCase							,					atol=1e-3      )      )
		# verify image_id
		__lowercase   							=	torch.tensor([3_9769]      )
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]							,					_lowerCAmelCase      )      )
		# verify is_crowd
		__lowercase   							=	torch.tensor([0, 0, 0, 0, 0, 0]      )
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]							,					_lowerCAmelCase      )      )
		# verify class_labels
		__lowercase   							=	torch.tensor([75, 75, 63, 65, 17, 17]      )
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]							,					_lowerCAmelCase      )      )
		# verify orig_size
		__lowercase   							=	torch.tensor([480, 640]      )
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]							,					_lowerCAmelCase      )      )
		# verify size
		__lowercase   							=	torch.tensor([800, 1066]      )
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]							,					_lowerCAmelCase      )      )







	@slow
	def 			_a (	self   :				Union[str, Any]      )		->			Union[str, Any]:
		"""simple docstring"""







		__lowercase   							=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
		with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""							,					"""r"""      ) as f:
			__lowercase   							=	json.loads(f.read()      )

		__lowercase   							=	{"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

		__lowercase   							=	pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""      )

		# encode them
		__lowercase   							=	DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-panoptic"""      )
		__lowercase   							=	image_processing(images=_lowerCAmelCase							,					annotations=_lowerCAmelCase							,					masks_path=_lowerCAmelCase							,					return_tensors="""pt"""      )

		# verify pixel values
		__lowercase   							=	torch.Size([1, 3, 800, 1066]      )
		self.assertEqual(encoding["""pixel_values"""].shape							,					_lowerCAmelCase      )

		__lowercase   							=	torch.tensor([0.2_796, 0.3_138, 0.3_481]      )
		self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]							,					_lowerCAmelCase							,					atol=1e-4      )      )

		# verify area
		__lowercase   							=	torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]      )
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]							,					_lowerCAmelCase      )      )
		# verify boxes
		__lowercase   							=	torch.Size([6, 4]      )
		self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape							,					_lowerCAmelCase      )
		__lowercase   							=	torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]      )
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]							,					_lowerCAmelCase							,					atol=1e-3      )      )
		# verify image_id
		__lowercase   							=	torch.tensor([3_9769]      )
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]							,					_lowerCAmelCase      )      )
		# verify is_crowd
		__lowercase   							=	torch.tensor([0, 0, 0, 0, 0, 0]      )
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]							,					_lowerCAmelCase      )      )
		# verify class_labels
		__lowercase   							=	torch.tensor([17, 17, 63, 75, 75, 93]      )
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]							,					_lowerCAmelCase      )      )
		# verify masks
		__lowercase   							=	82_2873
		self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()							,					_lowerCAmelCase      )
		# verify orig_size
		__lowercase   							=	torch.tensor([480, 640]      )
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]							,					_lowerCAmelCase      )      )
		# verify size
		__lowercase   							=	torch.tensor([800, 1066]      )
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]							,					_lowerCAmelCase      )      )







'''simple docstring'''


import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict


@pytest.mark.parametrize(
    'files'					,       [
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]					,       )
def     SCREAMING_SNAKE_CASE							(   a_						:				Tuple					,       a_						:				Dict      ):
  __a								=       tmp_path_factory.mktemp('dset_infos_dir'      )
  if "full:README.md" in files:
    with open(dataset_infos_dir / 'README.md'					,       'w'      ) as f:
      f.write('---\ndataset_info:\n  dataset_size: 42\n---'      )
  if "empty:README.md" in files:
    with open(dataset_infos_dir / 'README.md'					,       'w'      ) as f:
      f.write(''      )
    # we want to support dataset_infos.json for backward compatibility
  if "dataset_infos.json" in files:
    with open(dataset_infos_dir / 'dataset_infos.json'					,       'w'      ) as f:
      f.write('{"default": {"dataset_size": 42}}'      )
  __a								=       DatasetInfosDict.from_directory(a_      )
  assert dataset_infos
  assert dataset_infos["default"].dataset_size == 42


@pytest.mark.parametrize(
    'dataset_info'					,       [
        DatasetInfo(),
        DatasetInfo(
            description='foo'					,       features=Features({'a': Value('int32'      )}      )					,       builder_name='builder'					,       config_name='config'					,       version='1.0.0'					,       splits=[{'name': 'train'}]					,       download_size=42					,       ),
    ]					,       )
def     SCREAMING_SNAKE_CASE							(   a_						:				Optional[int]					,       a_						:				DatasetInfo      ):
  __a								=       str(a_      )
  dataset_info.write_to_directory(a_      )
  __a								=       DatasetInfo.from_directory(a_      )
  assert dataset_info == reloaded
  assert os.path.exists(os.path.join(a_					,       'dataset_info.json'      )      )


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       DatasetInfo(
      description='foo'					,       citation='bar'					,       homepage='https://foo.bar'					,       license='CC0'					,       features=Features({'a': Value('int32'      )}      )					,       post_processed={}					,       supervised_keys=()					,       task_templates=[]					,       builder_name='builder'					,       config_name='config'					,       version='1.0.0'					,       splits=[{'name': 'train', 'num_examples': 42}]					,       download_checksums={}					,       download_size=1337					,       post_processing_size=442					,       dataset_size=1234					,       size_in_bytes=1337 + 442 + 1234					,       )
  __a								=       dataset_info._to_yaml_dict()
  assert sorted(a_      ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML      )
  for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
    assert key in dataset_info_yaml_dict
    assert isinstance(dataset_info_yaml_dict[key]					,       (list, dict, int, str)      )
  __a								=       yaml.safe_dump(a_      )
  __a								=       yaml.safe_load(a_      )
  assert dataset_info_yaml_dict == reloaded


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       DatasetInfo()
  __a								=       dataset_info._to_yaml_dict()
  assert dataset_info_yaml_dict == {}


@pytest.mark.parametrize(
    'dataset_infos_dict'					,       [
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}      ),
        DatasetInfosDict({'my_config_name': DatasetInfo()}      ),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'					,       features=Features({'a': Value('int32'      )}      )					,       builder_name='builder'					,       config_name='config'					,       version='1.0.0'					,       splits=[{'name': 'train'}]					,       download_size=42					,       )
            }      ),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42      ),
                'v2': DatasetInfo(dataset_size=1337      ),
            }      ),
    ]					,       )
def     SCREAMING_SNAKE_CASE							(   a_						:				List[str]					,       a_						:				DatasetInfosDict      ):
  __a								=       str(a_      )
  dataset_infos_dict.write_to_directory(a_      )
  __a								=       DatasetInfosDict.from_directory(a_      )

  # the config_name of the dataset_infos_dict take over the attribute
  for config_name, dataset_info in dataset_infos_dict.items():
    __a								=       config_name
    # the yaml representation doesn't include fields like description or citation
    # so we just test that we can recover what we can from the yaml
    __a								=       DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()      )
  assert dataset_infos_dict == reloaded

  if dataset_infos_dict:
    assert os.path.exists(os.path.join(a_					,       'README.md'      )      )



